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---
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/FastThink-0.5B-Tiny
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
---
# **FastThink-0.5B-Tiny-GGUF**
> FastThink-0.5B-Tiny is a reasoning-focused model based on Qwen2.5. They have released a range of base language models and instruction-tuned language models, spanning from 0.5 billion to 72 billion parameters.
## Model Files
| File | Size | Description |
|------|------|-------------|
| README.md | 31 Bytes | Project documentation |
| config.json | 31 Bytes | Model configuration |
| .gitattributes | 2.39 kB | Git attributes configuration |
| FastThink-0.5B-Tiny.BF16.gguf | 994 MB | BFloat16 quantized model |
| FastThink-0.5B-Tiny.F16.gguf | 994 MB | Float16 quantized model |
| FastThink-0.5B-Tiny.F32.gguf | 1.98 GB | Float32 full precision model |
| FastThink-0.5B-Tiny.Q2_K.gguf | 339 MB | 2-bit K-quantized model |
| FastThink-0.5B-Tiny.Q3_K_L.gguf | 369 MB | 3-bit K-quantized model (Large) |
| FastThink-0.5B-Tiny.Q3_K_M.gguf | 355 MB | 3-bit K-quantized model (Medium) |
| FastThink-0.5B-Tiny.Q3_K_S.gguf | 338 MB | 3-bit K-quantized model (Small) |
| FastThink-0.5B-Tiny.Q4_K_M.gguf | 398 MB | 4-bit K-quantized model (Medium) |
| FastThink-0.5B-Tiny.Q4_K_S.gguf | 385 MB | 4-bit K-quantized model (Small) |
| FastThink-0.5B-Tiny.Q5_K_M.gguf | 420 MB | 5-bit K-quantized model (Medium) |
| FastThink-0.5B-Tiny.Q5_K_S.gguf | 413 MB | 5-bit K-quantized model (Small) |
| FastThink-0.5B-Tiny.Q6_K.gguf | 506 MB | 6-bit K-quantized model |
| FastThink-0.5B-Tiny.Q8_0.gguf | 531 MB | 8-bit quantized model |
## Quants Usage
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

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{
"model_type": "qwen2"
}

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{"framework": "pytorch", "task": "others", "allow_remote": true}